Controversie nella determinazione della prognosi Nicola D’Ostilio Carcinoma del rene: controversie Chieti, 27 Maggio 2016

Fattori prognostici e predittivi Caratteristiche anatomiche Caratteristiche istologiche Caratteristiche cliniche Caratteristiche molecolari

Caratteristiche anatomiche TNM PADUA RENAL C-INDEX

PADUA Preoperative Aspects and Dimension Used for an Anatomical Classification System Preoperative Aspect…. Eur Urol 2009 Ficarra V., et al

RENAL Radium, Exophytic/endophytic properties, Nearness of the tumor to the collecting system of sinus, Anterior/posterior, Location relative to the polar line The R.E.N.A.L. nefhrometry score: a comprehensive standardized system for quantitating renal tumor size, location and depth. J Urol 2009 Kutikov A., et al

C-INDEX Anterior/posterior position Kydney tumor location measurement usingthe c-index method. J Utol 2010 Simmons MN, et al

TNM Sopravvivenza a 5 anni T1:88-99% T2:70-82% T3: 10-60% T4: 20%

TNM TNM staging system for renal cell carcinoma: current status and future prospectives Ficarra V, Galfano A et al. Lancet Oncol. 2007

Controversie TNM pT2 (malattia locale-localmente avanzata) Cut-off <7-10 cm T2 classification for RCC. Can its accuracy be improved? J Urolol 2005 Frank I, Blute ML et al =11 cm Prognostic impact of tumor size on pT2 renal cell carcinoma: an international multicenter experience. J Urol 2007 Klatte T, Patard J,…Cindolo L, Ships L et al

TNM controversie T3 Prognostic rilevance of tumor size in T3a renal cell carcinoma: a multicentre experience. Eur Urol 2007 Lam JS, Klatte T,… Cindolo L, Ships L, et al T3a con cut-off: 7 cm A new staging system for locally advanced (T3-T4) renal cell carcinoma: a multicenter European study including 2000 patients. J Urol 2007 Ficarra V., Galfano A., Schips L., Cindolo L., et al

TMN controversie Infiltrazione delle via escretrice Prognostic rilevance of capsular involvement and collecting system invasion in stage I and II renal cell carcinoma. BJU Int 2007 Prognostic role of urinary collecting system invasion in renal cell carcinoma: a sistematic review and meta-analysis. Article open

TNM controversie T3b-c Prognosi uguale per invasione v. renale e v. cava sottodiaframmatica Prognosi peggiore per invasione v. cava sovradiaframmatica Il parametro più importante: infiltrazione grasso perirenale + invasione per contiguità del surrene

Caratteristiche istologiche Istotipo Grado nucleare di Fuhrman Componente sarcomatoide Invasione microvascolare Necrosi tumorale e interessamento sistema collettore

Istotipo Istotipi più frequenti - ca. a cellule chiare (prognosi peggiore, 43-83%) - ca. papillare (61-90%) - ca. cromofobo (80-100%) Comparisions of outcome and prognostic factures among histologic sutypes of renal cell carcinoma. AM J Surg Pathol 2003 (analisi univariata)

Ma… In analisi multivariata la significatività prognostica dell’istotipo viene persa suggerendo che lo stadio della malattia e grading del tumore abbiano un maggior impatto sulla prognosi rispetto alle caratteristiche istotipiche Prognostic value of histologic subtypes in renal cell carcinoma: a multicenter experience. J Clin Oncol 2005 Patard IJ, Leroy E., Cindolo L., et al

Caratteristiche cliniche PS Chirurgia Anemia, Neutropenia, trombocitosi

Sistemi prognostici UISS (University of California at Los Angeles Integrated Staging System IMDC (International Metastatic Renal-Cell Carcinoma Database Consortium)

UISS Modello che integra il TNM, l’ECOG PS ed il grado di Fuhrman Risk group assessment and clinical outcome algorithm to predict the natural hystory of patient with surgicaly resected renal carcinoma. J Clin Oncol 2002 Zisman A, et al

UISS Conserva il suo valore prognostico solo nella malattia localizzata Patard JJ et al. Use of the UISS to predict survival in renal cell carcinoma. J Clin Oncol 2004

IMDC PS Hb Ca Intervallo Conta neutrofili Conta piastrinica

IMDC Categorie di rischio: Favorevole (0 numero fattori, sopravvivenza a 2 anni 75%) Intermedia (1-2 fattori, sopravvivenza mediana 27 mesi, sopravvivenza a 2 anni 53%) Sfavorevole (3-6 fattori, sopravvivenza mediana 8,8 mesi, sopravvivenza a 2 anni 7%)

Predictive Biomarkers – Myth or Reality?

Prognostic versus predictive Biomarkers Prognostic Biomarkers Identify patients likely to have a good/poor disease outcome (OS) independent of treatment Predictive Biomarkers Identify patients who are likely to benefit from a particular treatment

«Ideal» Prognostic Score Easy to use Accurately distinguishes between patient groups with different outcomes Useful for informing patients Helpful for treatment decisions (e.g. cytoreductive nephrectomy, “active surveillance”)

Prognostic Scores: MSKCC Time from Start of IFN-α, years 2 16 14 4 10 8 6 Proportion Surviving 0.0 0.2 0.4 0.6 0.8 1.0 5 risk factors KPS <80 Time from diagnosis to IFN-α <1 year Low serum haemoglobin High corrected calcium (>2.5 mmol/L) High LDH (>1.5× ULN) Motzer et al. J Clin Oncol. 2002 Motzer et al. J Clin Oncol. 1999

Prognostic Scores: Heng criteria (IMDC) Heng et al. J Clin Oncol. 2009

Prognostic Scores: Heng criteria (IMDC) 0 risk factors 1 – 2 risk factors 3 – 6 risk factors N= 645 Heng et al. J Clin Oncol. 2009

IMDC Prognostic Factors: Validation 0 risk factors: Favorable 43 mo N= 849 1-2 risk factors:Intermediate 23 mo 3-6 risk factors: Poor 8 mo Heng et al. Lancet Oncol. 2013

Parameters of different Prognostic Models Clinical Biological Table modified from Heng et al. Lancet Oncol. 2013 Motzer RJ et al. J Clin Oncol. 2002;20:289-296;Negrier S et al. Ann Oncol. 2002;13:1460-1468; Mekhail T et al. J Clin Oncol. 2005;23:832-841; Manola J et al. Clin Cancer Res. 2011;17:5443-5450.; Choueiri TK et al. Cancer. 2007;110:543-550.; Heng DY et al. J Clin Oncol. 2009;27:5794-5799.

IMDC in non-clear cell RCC P<0.0001 Kroeger N et al. Cancer. 2013;119:2999-3006

Heng et al. Eur Urol 2014

Time to recurrence is a significant predictor of cancer- specific survival after recurrence in patients with recurrent renal cell carcinoma: result from a comprehensive multi-centre database (CORONA/SATURN-Project) Brookman-May SD,…, Cindolo L, et al. BJU Int. 2013

Potential Predictive Biomarkers Under Investigation

PD-L1 Expression

PD-L1 Interacts with PD-1 to Suppress T-Cell Response Antigen-experienced T cell PD-L1 PD-1 No Signal 2 Tissue Inflammation Signal 1 Costim. Receptor CD28 Traffic to periphery Ligand T-cell priming Signal 1 DC PD-L1 Signal 2 PD-L1 is overexpressed in a variety of solid tumours including RCC1 Interruption of Signal 2 leads to T-cell non-responsiveness

Problems with Biomarker Development Measurement Issues, eg, PD-L1 PD-L1 expression is measured and interpreted in a variety of ways, which limit cross-study comparisons and validation PD-L1 antibodies1,2 Many different antibodies are used in clinical studies Two commonly used antibodies had poor concordance in NSCLC biopsies1 PD-L1 assays1 IHC interpretation is subjective Some clinical studies use proprietary assays, limiting validation/ comparisons PD-L1 cutoff2 Percentage cut-off varies widely (1% - 50%) H-score is sometimes used Tissue type measured for PD-L1 varies across clinical studies1 Tumour epithelial cells Tumour epithelial cell membrane Immune cells of peritumoural stroma McLaughlin J et al [Published online ahead of print]. JAMA Oncol. 2015 Nov 12 Patel P et al. Mol Cancer Ther. 2015;14(4):847-56.

PD-L1 is a Prognostic Marker in aRCC Meta-Analysis Risk of Death in PD-L1+ vs PD-L1- RCC patients (various cut-offs for PD-L1)a a PD-L1 cut-off using IHC was: ≥5% (2 studies), ≥10% (1 study), H-score >55 (1 study), not-reported (1 study), and the remaining study used continuous ELISA to assess PD-L1 Reprinted with permission from Iacovelli R et al. [Published online ahead of print] Targ Oncol. 2015 Oct 2

Association of OS with PD-L1 expression status on tumor cell membrane. Toni K. Choueiri et al. Clin Cancer Res 2015;21:1071-1077 ©2015 by American Association for Cancer Research

Probability of Overall Survival Probability of Overall Survival PD-L1 was not Predictive of Nivolumab Benefit in aRCC in CheckMate-025 (1% Cut-off) PD-L1 <1% (n=76%) PD-L1 ≥1% (n=24%) Median OS, months (95% Cl) Nivolumab 21.8 (16.5–28.1) Everolimus 18.8 (11.9–19.9) Median OS, months (95% Cl) Nivolumab 27.4 (21.4 – NE) Everolimus 21.2 (17.7 – 26.2) 3 6 9 12 15 18 21 24 27 30 33 0.0 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 Probability of Overall Survival Nivolumab Everolimus HR (95% CI): 0.77 (0.60–0.97) 3 6 9 12 15 18 21 24 27 30 33 0.0 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 Probability of Overall Survival Nivolumab Everolimus HR (95% CI): 0.79 (0.53−1.17) No. at Risk Months No. at Risk Months Nivolumab 94 86 79 73 66 58 45 31 18 4 1 Everolimus 97 77 68 59 52 47 40 19 9 276 265 245 233 210 189 145 94 48 22 2 299 267 238 214 200 192 137 92 51 16 1 Reprinted with permission from Motzer RJ et al. N Engl J Med. 2015;373(19):1803-13

PD-L1 Expression did not Predict Benefit with Atezolizumab in a Phase 1 Trial in RCC 1-year OS 2-year OS <1% PD-L1 (n=22) 81% 65% ≥1% PD-L1 (n=33) 80% 51% PD-L1 Patients , N (%) ORR, % Median PFS, months (95% CI) Median OS, months <1% 22 6 (18) 5.6 (2.8-9.0) NR (20.0-NR) ≥1% 33 2 (9) 4.5 (1.3-8.1) 28.8 (16.3-28.9) Reprinted with permission from McDermott DF et al. J Clin Oncol. 2016;34(8):833-42.

Validation of IMDC prognostic model for first-line pazopanib in metastatic renal carcinoma: the Spanish Oncologic Genitourinary Group (SOGUG) SPAZO study Perez-Valderramma B et al. Ann Oncol 2016

Final results from the large sunitinib global expanded-access trial in metastatic renal cell carcinoma Gore ME, Porta C, Bracarda S, et al. Br J Cancer 2015

The International Metastatic Renal Cell Carcinoma Database Consortium model as a prognostic tool in patients with metastatic renal cell carcinoma previously treated with first-line targeted therapy: a population-base study Ko JJ et al. Lancet Oncol 2015

Summary Prognostic models: Predictive Biomarkers Aid patient counselling and therapy planning IMDC and MSKCC are the most widely used prognostic models, with different baseline factors but similar patient outcomes Predictive Biomarkers There are no validated predictive biomarkers to aid treatment selection in aRCC Many candidate predictive biomarkers have been identified, and need to be validated PD-L1 may not be predictive of PD-1/PD-L1 inhibitor benefit, and may in fact be prognostic VEGFR-1 polymorphisms may predict bevacizumab benefit PBRM1 and KDM5C mutation status may predict benefit with everolimus and sunitinib, respectively IL-8 polymorphisms may predict benefit with VEGFR TKI therapy High circulating IL-18 levels may predict benefit with sunitinib vs everolimus Limitations include tumour heterogeneity, unreliable detection assays, and lack of patients numbers

Predictive Biomarkers – Myth or Reality?